402 citations · 913 across the 36 of their papers we have counts for
41 papers
Efficiently Factorizing Boolean Matrices using Proximal Gradient Descent
Sebastian Dalleiger, Jilles Vreeken
Addressing the interpretability problem of NMF on Boolean data, Boolean Matrix Factorization (BMF) uses Boolean algebra to decompose the input into low-rank Boolean factor matrices…
Preserving local densities in low-dimensional embeddings
Jonas Fischer, Rebekka Burkholz, Jilles Vreeken
Low-dimensional embeddings and visualizations are an indispensable tool for analysis of high-dimensional data. State-of-the-art methods, such as tSNE and UMAP, excel in unveiling l…
All the World's a (Hyper)Graph: A Data Drama
Corinna Coupette, Jilles Vreeken, Bastian Rieck
We introduce Hyperbard, a dataset of diverse relational data representations derived from Shakespeare's plays. Our representations range from simple graphs capturing character co-o…
Differentially Describing Groups of Graphs
Corinna Coupette, Sebastian Dalleiger, Jilles Vreeken
How does neural connectivity in autistic children differ from neural connectivity in healthy children or autistic youths? What patterns in global trade networks are shared across c…
Label-Descriptive Patterns and Their Application to Characterizing Classification Errors
Michael Hedderich, Jonas Fischer, Dietrich Klakow +1
State-of-the-art deep learning methods achieve human-like performance on many tasks, but make errors nevertheless. Characterizing these errors in easily interpretable terms gives i…
Federated Learning from Small Datasets
Michael Kamp, Jonas Fischer, Jilles Vreeken
Federated learning allows multiple parties to collaboratively train a joint model without sharing local data. This enables applications of machine learning in settings of inherentl…
Graph Similarity Description: How Are These Graphs Similar?
Corinna Coupette, Jilles Vreeken
How do social networks differ across platforms? How do information networks change over time? Answering questions like these requires us to compare two or more graphs. This task is…
Formally Justifying MDL-based Inference of Cause and Effect
Alexander Marx, Jilles Vreeken
The algorithmic independence of conditionals, which postulates that the causal mechanism is algorithmically independent of the cause, has recently inspired many highly successful a…
Factoring out prior knowledge from low-dimensional embeddings
Edith Heiter, Jonas Fischer, Jilles Vreeken
Low-dimensional embedding techniques such as tSNE and UMAP allow visualizing high-dimensional data and therewith facilitate the discovery of interesting structure. Although they ar…
Data-driven equation for drug-membrane permeability across drugs and membranes
Arghya Dutta, Jilles Vreeken, Luca M. Ghiringhelli +1
Drug efficacy depends on its capacity to permeate across the cell membrane. We consider the prediction of passive drug-membrane permeability coefficients. Beyond the widely recogni…
Discovering Reliable Causal Rules
Kailash Budhathoki, Mario Boley, Jilles Vreeken
We study the problem of deriving policies, or rules, that when enacted on a complex system, cause a desired outcome. Absent the ability to perform controlled experiments, such rule…
What is Normal, What is Strange, and What is Missing in a Knowledge Graph: Unified Characterization via Inductive Summarization
Caleb Belth, Xinyi Zheng, Jilles Vreeken +1
Knowledge graphs (KGs) store highly heterogeneous information about the world in the structure of a graph, and are useful for tasks such as question answering and reasoning. Howeve…
Discovering Reliable Correlations in Categorical Data
Panagiotis Mandros, Mario Boley, Jilles Vreeken
In many scientific tasks we are interested in discovering whether there exist any correlations in our data. This raises many questions, such as how to reliably and interpretably me…
Summarizing Data Succinctly with the Most Informative Itemsets
Michael Mampaey, Jilles Vreeken, Nikolaj Tatti
Knowledge discovery from data is an inherently iterative process. That is, what we know about the data greatly determines our expectations, and therefore, what results we would fin…
Testing Conditional Independence on Discrete Data using Stochastic Complexity
Alexander Marx, Jilles Vreeken
Testing for conditional independence is a core aspect of constraint-based causal discovery. Although commonly used tests are perfect in theory, they often fail to reject independen…
Discovering Descriptive Tile Trees by Mining Optimal Geometric Subtiles
Nikolaj Tatti, Jilles Vreeken
When analysing binary data, the ease at which one can interpret results is very important. Many existing methods, however, discover either models that are difficult to read, or ret…
The Long and the Short of It: Summarising Event Sequences with Serial Episodes
Nikolaj Tatti, Jilles Vreeken
An ideal outcome of pattern mining is a small set of informative patterns, containing no redundancy or noise, that identifies the key structure of the data at hand. Standard freque…
Finding Good Itemsets by Packing Data
Nikolaj Tatti, Jilles Vreeken
The problem of selecting small groups of itemsets that represent the data well has recently gained a lot of attention. We approach the problem by searching for the itemsets that co…
Comparing Apples and Oranges: Measuring Differences between Exploratory Data Mining Results
Nikolaj Tatti, Jilles Vreeken
Deciding whether the results of two different mining algorithms provide significantly different information is an important, yet understudied, open problem in exploratory data mini…
We Are Not Your Real Parents: Telling Causal from Confounded using MDL
David Kaltenpoth, Jilles Vreeken
Given data over variables we consider the problem of finding out whether jointly causes or whether they are all confounded by an unobserved latent variab…
Discovering Reliable Dependencies from Data: Hardness and Improved Algorithms
Panagiotis Mandros, Mario Boley, Jilles Vreeken
The reliable fraction of information is an attractive score for quantifying (functional) dependencies in high-dimensional data. In this paper, we systematically explore the algorit…
Causal Discovery by Telling Apart Parents and Children
Alexander Marx, Jilles Vreeken
We consider the problem of inferring the directed, causal graph from observational data, assuming no hidden confounders. We take an information theoretic approach, and make three m…
Towards Plausible Graph Anonymization
Yang Zhang, Mathias Humbert, Bartlomiej Surma +3
Social graphs derived from online social interactions contain a wealth of information that is nowadays extensively used by both industry and academia. However, as social graphs con…
Telling Cause from Effect using MDL-based Local and Global Regression
Alexander Marx, Jilles Vreeken
We consider the fundamental problem of inferring the causal direction between two univariate numeric random variables and from observational data. The two-variable case is…